MySQL为什么"错误"选择代价更大的索引
1.问题描述
群友提出问题，表里有两个列c1、c2，分别为INT、VARCHAR类型，且分别创建了unique key。
SQL查询的条件是 WHERE c1 = ? AND c2 = ?，用EXPLAIN查看执行计划，发现优化器优先选择了VARCHAR类型的c2列索引。
他表示很不理解，难道不应该选择看起来代价更小的INT类型的c1列吗？
2.问题复现
创建测试表t1：
[root@yejr.run]> CREATE TABLE `t1` ( `c1` int NOT NULL AUTO_INCREMENT, `c2` int unsigned NOT NULL, `c3` varchar(20) NOT NULL, `c4` varchar(20) NOT NULL, PRIMARY KEY (`c1`), UNIQUE KEY `k3` (`c3`), UNIQUE KEY `k2` (`c2`) ) ENGINE=InnoDB;
利用 mysql_random_data_load 写入一万行数据：
mysql_random_data_load -h127.0.0.1 -uuser -p123456 wang t1 10000
查看执行计划：
mysql> EXPLAIN SELECT * FROM t1 WHERE c2 = 950391508 AND c3 = 'Patrick Price'\G *************************** 1. row *************************** id: 1 select_type: SIMPLE table: t1 partitions: NULL type: const possible_keys: k3,k2 key: k3 key_len: 82 ref: const rows: 1 filtered: 100.00 Extra: NULL 1 row in set, 1 warning (0.01 sec) mysql>
可以看到优化器的确选择了 k3 索引，而非"预期"的 k2 索引，这是为什么呢？
3.问题分析
其实原因很简单粗暴：优化器认为这两个索引选择的代价都是一样的，只是优先选中排在前面的那个索引而已。
再建一个相同的表 t2，只不过把 k2、k3 的索引创建顺序对调下：
[root@yejr.run]> CREATE TABLE `t2` ( `c1` int NOT NULL AUTO_INCREMENT, `c2` int unsigned NOT NULL, `c3` varchar(20) NOT NULL, `c4` varchar(20) NOT NULL, PRIMARY KEY (`c1`), UNIQUE KEY `k2` (`c2`), UNIQUE KEY `k3` (`c3`) ) ENGINE=InnoDB;
再次利用利用 mysql_random_data_load 写入一万行数据：
mysql_random_data_load -h127.0.0.1 -uuser -p123456 wang t2 10000
再查看执行计划：
mysql> EXPLAIN SELECT * FROM t2 WHERE c2 = 1051998464 AND c3 = 'Tammy Bell'\G *************************** 1. row *************************** id: 1 select_type: SIMPLE table: t2 partitions: NULL type: const possible_keys: k2,k3 key: k2 key_len: 4 ref: const rows: 1 filtered: 100.00 Extra: NULL 1 row in set, 1 warning (0.01 sec) mysql>
我们利用 EXPLAIN ANALYZE 来查看下两次执行计划的代价对比：
-- 查看t1表执行计划代价 mysql> EXPLAIN ANALYZE SELECT * FROM t1 WHERE c2 = 950391508 AND c3 = 'Patrick Price'\G *************************** 1. row *************************** EXPLAIN: -> Rows fetched before execution (cost=0.00..0.00 rows=1) (actual time=0.002..0.003 rows=1 loops=1) 1 row in set (0.00 sec) mysql> -- 查看t2表执行计划代价 mysql> EXPLAIN ANALYZE SELECT * FROM t2 WHERE c2 = 1051998464 AND c3 = 'Tammy Bell'\G *************************** 1. row *************************** EXPLAIN: -> Rows fetched before execution (cost=0.00..0.00 rows=1) (actual time=0.002..0.003 rows=1 loops=1) 1 row in set (0.00 sec) mysql>
可以看到，很明显代价都是一样的。
再利用 OPTIMIZE_TRACE 查看执行计划，也能看到两个SQL的代价是一样的（重点查看rows_estimation部分）：
查看t1表执行计划代价
--开启OPTIMIZER_TRACE功能，并设置要展示的数据条目数： set optimizer_trace="enabled=on",end_markers_in_json=on; -- 查看t1表执行计划代价 mysql> SELECT * FROM t1 WHERE c2 = 950391508 AND c3 = 'Patrick Price'\G *************************** 1. row *************************** c1: 55 c2: 950391508 c3: Patrick Price c4: Roger Harvey 1 row in set (0.00 sec) mysql> mysql> SELECT * FROM INFORMATION_SCHEMA.OPTIMIZER_TRACE limit 30 \G; *************************** 1. row *************************** QUERY: SELECT * FROM t1 WHERE c2 = 950391508 AND c3 = 'Patrick Price' TRACE: { "steps": [ { "join_preparation": { "select#": 1, "steps": [ { "expanded_query": "/* select#1 */ select `t1`.`c1` AS `c1`,`t1`.`c2` AS `c2`,`t1`.`c3` AS `c3`,`t1`.`c4` AS `c4` from `t1` where ((`t1`.`c2` = 950391508) and (`t1`.`c3` = 'Patrick Price'))" } ] /* steps */ } /* join_preparation */ }, { "join_optimization": { "select#": 1, "steps": [ { "condition_processing": { "condition": "WHERE", "original_condition": "((`t1`.`c2` = 950391508) and (`t1`.`c3` = 'Patrick Price'))", "steps": [ { "transformation": "equality_propagation", "resulting_condition": "(multiple equal(950391508, `t1`.`c2`) and multiple equal('Patrick Price', `t1`.`c3`))" }, { "transformation": "constant_propagation", "resulting_condition": "(multiple equal(950391508, `t1`.`c2`) and multiple equal('Patrick Price', `t1`.`c3`))" }, { "transformation": "trivial_condition_removal", "resulting_condition": "(multiple equal(950391508, `t1`.`c2`) and multiple equal('Patrick Price', `t1`.`c3`))" } ] /* steps */ } /* condition_processing */ }, { "substitute_generated_columns": { } /* substitute_generated_columns */ }, { "table_dependencies": [ { "table": "`t1`", "row_may_be_null": false, "map_bit": 0, "depends_on_map_bits": [ ] /* depends_on_map_bits */ } ] /* table_dependencies */ }, { "ref_optimizer_key_uses": [ { "table": "`t1`", "field": "c3", "equals": "'Patrick Price'", "null_rejecting": true }, { "table": "`t1`", "field": "c2", "equals": "950391508", "null_rejecting": true } ] /* ref_optimizer_key_uses */ }, { "rows_estimation": [ { "table": "`t1`", "rows": 1, "cost": 1, "table_type": "const", "empty": false } ] /* rows_estimation */ }, { "condition_on_constant_tables": "true", "condition_value": true }, { "attaching_conditions_to_tables": { "original_condition": "true", "attached_conditions_computation": [ ] /* attached_conditions_computation */, "attached_conditions_summary": [ ] /* attached_conditions_summary */ } /* attaching_conditions_to_tables */ }, { "refine_plan": [ ] /* refine_plan */ } ] /* steps */ } /* join_optimization */ }, { "join_execution": { "select#": 1, "steps": [ ] /* steps */ } /* join_execution */ } ] /* steps */ } MISSING_BYTES_BEYOND_MAX_MEM_SIZE: 0 INSUFFICIENT_PRIVILEGES: 0 1 row in set (0.00 sec) ERROR: No query specified mysql> --分析完成，关闭OPTIMIZER_TRACE SET optimizer_trace="enabled=off";
查看t2表执行计划代价
--开启OPTIMIZER_TRACE功能，并设置要展示的数据条目数： set optimizer_trace="enabled=on",end_markers_in_json=on; -- 查看t2表执行计划代价 mysql> SELECT * FROM t2 WHERE c2 = 1051998464 AND c3 = 'Tammy Bell'\G *************************** 1. row *************************** c1: 143 c2: 1051998464 c3: Tammy Bell c4: Jane Perry 1 row in set (0.00 sec) mysql> mysql> SELECT * FROM INFORMATION_SCHEMA.OPTIMIZER_TRACE limit 30 \G; *************************** 1. row *************************** QUERY: SELECT * FROM t2 WHERE c2 = 1051998464 AND c3 = 'Tammy Bell' TRACE: { "steps": [ { "join_preparation": { "select#": 1, "steps": [ { "expanded_query": "/* select#1 */ select `t2`.`c1` AS `c1`,`t2`.`c2` AS `c2`,`t2`.`c3` AS `c3`,`t2`.`c4` AS `c4` from `t2` where ((`t2`.`c2` = 1051998464) and (`t2`.`c3` = 'Tammy Bell'))" } ] /* steps */ } /* join_preparation */ }, { "join_optimization": { "select#": 1, "steps": [ { "condition_processing": { "condition": "WHERE", "original_condition": "((`t2`.`c2` = 1051998464) and (`t2`.`c3` = 'Tammy Bell'))", "steps": [ { "transformation": "equality_propagation", "resulting_condition": "(multiple equal(1051998464, `t2`.`c2`) and multiple equal('Tammy Bell', `t2`.`c3`))" }, { "transformation": "constant_propagation", "resulting_condition": "(multiple equal(1051998464, `t2`.`c2`) and multiple equal('Tammy Bell', `t2`.`c3`))" }, { "transformation": "trivial_condition_removal", "resulting_condition": "(multiple equal(1051998464, `t2`.`c2`) and multiple equal('Tammy Bell', `t2`.`c3`))" } ] /* steps */ } /* condition_processing */ }, { "substitute_generated_columns": { } /* substitute_generated_columns */ }, { "table_dependencies": [ { "table": "`t2`", "row_may_be_null": false, "map_bit": 0, "depends_on_map_bits": [ ] /* depends_on_map_bits */ } ] /* table_dependencies */ }, { "ref_optimizer_key_uses": [ { "table": "`t2`", "field": "c2", "equals": "1051998464", "null_rejecting": true }, { "table": "`t2`", "field": "c3", "equals": "'Tammy Bell'", "null_rejecting": true } ] /* ref_optimizer_key_uses */ }, { "rows_estimation": [ { "table": "`t2`", "rows": 1, "cost": 1, "table_type": "const", "empty": false } ] /* rows_estimation */ }, { "condition_on_constant_tables": "true", "condition_value": true }, { "attaching_conditions_to_tables": { "original_condition": "true", "attached_conditions_computation": [ ] /* attached_conditions_computation */, "attached_conditions_summary": [ ] /* attached_conditions_summary */ } /* attaching_conditions_to_tables */ }, { "refine_plan": [ ] /* refine_plan */ } ] /* steps */ } /* join_optimization */ }, { "join_execution": { "select#": 1, "steps": [ ] /* steps */ } /* join_execution */ } ] /* steps */ } MISSING_BYTES_BEYOND_MAX_MEM_SIZE: 0 INSUFFICIENT_PRIVILEGES: 0 1 row in set (0.00 sec) ERROR: No query specified mysql> --分析完成，关闭OPTIMIZER_TRACE SET optimizer_trace="enabled=off";
所以，优化器认为选择哪个索引都是一样的，就看哪个索引排序更靠前。
从执行SELECT时的debug trace结果也能佐证：
-- 1、 T1表，k3索引在前面 PRIMARY KEY (`c1`), UNIQUE KEY `k3` (`c3`), UNIQUE KEY `k2` (`c2`) T@2: | | | | | | | | opt: (null): starting struct T@2: | | | | | | | | opt: table: "`t1`" T@2: | | | | | | | | opt: field: "c3" (C3在前面，因此最后使用k3) T@2: | | | | | | | | >convert_string T@2: | | | | | | | | | >alloc_root T@2: | | | | | | | | | | enter: root: 0x40a8068 T@2: | | | | | | | | | | exit: ptr: 0x4b41ab0 T@2: | | | | | | | | | <alloc_root 304 T@2: | | | | | | | | <convert_string 2610 T@2: | | | | | | | | opt: equals: "'Louise Garrett'" T@2: | | | | | | | | opt: null_rejecting: 0 T@2: | | | | | | | | opt: (null): ending struct T@2: | | | | | | | | opt: Key_use: optimize= 0 used_tables=0x0 ref_table_rows= 18446744073709551615 keypart_map= 1 T@2: | | | | | | | | opt: (null): starting struct T@2: | | | | | | | | opt: table: "`t1`" T@2: | | | | | | | | opt: field: "c2" T@2: | | | | | | | | opt: equals: "22896242" T@2: | | | | | | | | opt: null_rejecting: 0 T@2: | | | | | | | | opt: null_rejecting: 0 T@2: | | | | | | | | opt: (null): ending struct T@2: | | | | | | | | opt: Key_use: optimize= 0 used_tables=0x0 ref_table_rows= 18446744073709551615 keypart_map= 1 T@2: | | | | | | | | opt: (null): starting struct T@2: | | | | | | | | opt: table: "`t1`" T@2: | | | | | | | | opt: field: "c2" T@2: | | | | | | | | opt: equals: "22896242" T@2: | | | | | | | | opt: null_rejecting: 0 T@2: | | | | | | | | opt: (null): ending struct T@2: | | | | | | | | opt: ref_optimizer_key_uses: ending struct T@2: | | | | | | | | opt: (null): ending struct -- 2、 T2表，k2索引在前面 PRIMARY KEY (`c1`), UNIQUE KEY `k2` (`c2`), UNIQUE KEY `k3` (`c3`) T@2: | | | | | | | | opt: (null): starting struct T@2: | | | | | | | | opt: table: "`t2`" T@2: | | | | | | | | opt: field: "c2" （C2在前面因此使用k2索引） T@2: | | | | | | | | opt: equals: "22896242" T@2: | | | | | | | | opt: null_rejecting: 0 T@2: | | | | | | | | opt: (null): ending struct T@2: | | | | | | | | opt: Key_use: optimize= 0 used_tables=0x0 ref_table_rows= 18446744073709551615 keypart_map= 1 T@2: | | | | | | | | opt: (null): starting struct T@2: | | | | | | | | opt: table: "`t2`" T@2: | | | | | | | | opt: field: "c3" T@2: | | | | | | | | >convert_string T@2: | | | | | | | | | >alloc_root T@2: | | | | | | | | | | enter: root: 0x40a8068 T@2: | | | | | | | | | | exit: ptr: 0x4b41ab0 T@2: | | | | | | | | | <alloc_root 304 T@2: | | | | | | | | <convert_string 2610 T@2: | | | | | | | | opt: equals: "'Louise Garrett'" T@2: | | | | | | | | opt: null_rejecting: 0 T@2: | | | | | | | | opt: (null): ending struct T@2: | | | | | | | | opt: ref_optimizer_key_uses: ending struct T@2: | | | | | | | | opt: (null): ending struct
4.问题延伸
到这里，我们不禁有疑问，这两个索引的代价真的是一样吗？
就让我们用 mysqlslap 来做个简单对比测试吧：
-- 测试1：对c2列随机point select [root@ocky-Linux-8-4 ~]# mysqlslap -h127.0.0.1 -uuser -p123456 --no-drop --create-schema wang -i 3 --number-of-queries 1000000 -q "set @xid = cast(round(rand()*2147265929) as unsigned); select * from t1 where c2 = @xid" -c 8 mysqlslap: [Warning] Using a password on the command line interface can be insecure. Benchmark Average number of seconds to run all queries: 40.534 seconds #运行所有语句的平均秒数 Minimum number of seconds to run all queries: 39.465 seconds #运行所有语句的最小秒数 Maximum number of seconds to run all queries: 41.746 seconds #运行所有语句的最大秒数 Number of clients running queries: 8 #客户端数量 Average number of queries per client: 125000 #每个客户端运行查询的平均数 -- 测试2：对c3列随机point select [root@ocky-Linux-8-4 ~]# mysqlslap -h127.0.0.1 -uuser -p123456 --no-drop --create-schema wang -i 3 --number-of-queries 1000000 -q "set @xid = concat('u',cast(round(rand()*2147265929) as unsigned)); select * from t1 where c3 = @xid" -c 8 mysqlslap: [Warning] Using a password on the command line interface can be insecure. Benchmark Average number of seconds to run all queries: 42.275 seconds Minimum number of seconds to run all queries: 41.714 seconds Maximum number of seconds to run all queries: 43.138 seconds Number of clients running queries: 8 Average number of queries per client: 125000
可以看到，如果是走 c3 列索引，耗时会比走 c2 列索引多出来约 4%（在我的环境下测试的结果，不同环境、不同数据量可能也不同）。
看来，MySQL优化器还是有必要进一步提高的哟 ：）
测试使用版本：MySQL 8.0.27（MySQL 5.6.39结果亦是如此）。
原文链接：https://mp.weixin.qq.com/s/8pwqetWeORuDgs2VSSfEkg
